DIAGNOSTICS FOR STRATIFIED CLINICAL TRIALS IN PROPORTIONAL ODDS MODELS
|
|
- Emily Gardner
- 5 years ago
- Views:
Transcription
1 DIAGNOSTICS FOR STRATIFIED CLINICAL TRIALS IN PROPORTIONAL ODDS MODELS Ivy Liu and Dong Q. Wang School of Mathematics, Statistics and Computer Science Victoria University of Wellington New Zealand Corresponding author: Ivy Liu Key Words: clinical trials; cumulative odds ratio; diagnostics; influence measure; Mantel- Haenszel estimator; ordinal response; proportional odds model; random effects; and strata deletion. ABSTRACT For stratified clinical trials with an ordinal response, this article considers two diagnostics strategies for evaluating the heterogeneity of the ordinal odds ratios across multi-centers in proportional odds models. The first strategy assumes multi-centers as a sample from a population and then uses a proportional odds model allowing random effects to model the heterogeneity among the ordinal log odds ratios. It gives an overall view of the heterogeneity across multi-centers. The second strategy applies the influence measure to the Mantel- Haenszel type estimators of the ordinal odds ratios. It shows the detail of heterogeneity in each of the multi-centers. At the end, this article uses a multi-center clinical trials example to illustrate both strategies. 1. INTRODUCTION This article considers diagnostics methods for stratified multi-center clinical trials when the response variable has a natural ordering (e.g., better, unchanged, and worse). For a clinical trial study, the odds ratio is commonly used to describe the relationship between treatments and response. When the response is ordinal, the ordinal odds ratios that sum- 1
2 marize the relationship across different centers are considered based on a proportional odds model. Diagnostic methods play an important role in categorical data analysis. For instance, Grizzle and Williams (1972) explored a general diagnostic approach to the analysis of categorical data for fitting a loglinear model. Pregibon (1981) developed diagnostic measures for a maximum likelihood fit of a logistic regression model. Andersen (1992) discussed diagnostics for categorical data in the Goodman association model. Wang, Critchley and Liu (2004) considered the diagnostic method for a contingency table assuming a clustered sampling model. A well known diagnostic measure, Cook distance, is widely used for linear regression models (Cook and Weisberg, 1982) and multivariate analysis (Wang and Critchley, 2000 and 2003). This article uses two diagnostics strategies for evaluating the heterogeneity of the ordinal odds ratios across multi-centers in proportional odds models. The strategies are the following: (1) Using a proportional odds model (McCullagh, 1980) allowing random effects that assumes the multi-centers as a sample from a population (Hartzel, Liu and Agresti, 2001). The model permits heterogeneity in the conditional associations between treatments and response. The estimate of the mean for the association effects describes the average relationship between treatments and response across centers. The variance estimate for the association effects describes the heterogeneity of the relationship across centers. (2) Using Mantel-Haenszel (Mantel and Haenszel, 1959) type estimators to evaluate the ordinal odds ratios in a proportional odds model (Liu and Agresti, 1999; Liu, 2003). This technique summarizes the association effects across the centers using ordinal odds ratios. Then, we propose an influence measure to indicate the detail of the heterogeneity for the ordinal odds ratio in each of the centers. When each center has very few observations, the second strategy is better than the first one, because the model fitting algorithm for the proportional odds model with random effects often has computational problems for sparse data. However, the first strategy gives an overall view of the heterogeneity while the second gives the detail of the heterogeneity in each of the centers. 2
3 Section 2 introduces the proportional odds model used for fitting stratified clinical trials data with an ordinal response. It also shows the Mantel-Haenszel type estimators of the ordinal odds ratios that describe the conditional associations between treatments and response. Section 3 discusses the first strategy that determines the level of heterogeneity using a proportional odds model allowing random effects. Section 4 proposes the second strategy where a influence measure is used to explore the detail of heterogeneity for the ordinal log odds ratios across centers. Section 5 gives an example using these strategies. 2. ORDINAL ODDS RATIOS IN A PROPORTIONAL ODDS MODEL When the response is ordinal (e.g., better, unchanged, and worse), the most popular model uses logits of cumulative probabilities. The model is often called the proportional odds model (McCullagh, 1980). For instance, in a clinical trial study, one can compare r treatments on a c-level ordinal response for data from K clinical centers using the model. Let π ijk denote the probability that the response is at level j when a patient at center k received treatment i, where π i1k + + π ick = 1 for all i = 1,..., r and k = 1,..., K. The cumulative probabilities are denoted by πijk = π i1k π ijk for all i = 1,..., r, j = 1,..., c 1, and k = 1,..., K. The proportional odds model considered has the form ( ) π ijk log = α 1 πijk j + γ k + β i i = 1,..., r, j = 1,..., c 1, k = 1,..., K, (1) with a constraint γ K = β r = 0. The model is simply a logit model when the response is binary. This article uses an example provided by Merck Research Laboratories for a double-blind, parallel-group preliminary clinical study conducted at 21 centers, where patients suffering from asthma were randomly assigned to 3 different treatments (2mg active drug, 10mg active drug, and placebo). At the end of the study, the doctors described the patients change in condition using an ordinal scale from better to worse (1 to 4). Table 1 shows the results of the doctors evaluations associated with the treatments and is presented as 21 separate 3 4 tables. Such a study might use many clinics because of the time it takes each clinic center to recruit many patients. Therefore, the three-way table might then have many strata but 3
4 few observations per stratum. It leads to a sparse data set. In the proportional odds model (1), parameters {β i } describe the treatment effects given clinics. For each clinic center, the odds that the evaluation for treatment i falls below any fixed level are exp(β i ) times the odds for treatment r. We refer to the ordinal odds ratio exp(β i ) as the cumulative odds ratio, since the model is based on the cumulative probabilities. When fitting the model, the maximum likelihood (ML) estimators of {β i } perform badly for sparse data. Liu and Agresti (1996) and Liu (2003) have shown that the ML method tends to overestimate the effects. This is not surprising, because it also happens for a binary response case (Andersen 1980, p. 244 and Ghosh, 1995). The standard asymptotic properties of ML estimators fail when the number of nuisance parameters (such as {γ k }) grows at the same rate as the sample size (Neyman and Scott, 1948). Alternatively, the Mantel-Haenszel (MH) type method provides better estimators for {β i } (Liu and Agresti, 1996; Liu, 2003). Liu (2003) extended the ordinary MH estimator (Mantel and Haenszel, 1959) for a common odds ratio for several 2 2 tables to the case of several r c tables. Like the ordinary MH estimator the extended MH-type estimator is consistent under both the ordinary asymptotics in which the number of strata is fixed and also the sparse asymptotics in which the number of strata grows with the sample size. The estimator is referred to a dually consistent estimator. We assume that each r c table is formed by r independent multinomial samples (X 1jk, X 2jk,..., X rjk, j = 1,..., c) with sample sizes denoted by (n 1k, n 2k,..., n rk ). The total sample size in each stratum is denoted by N k = n 1k n rk, for k = 1,..., K. Let cumulative counts be X ijk = X i1k X ijk and let R ih jk = X ijk (n hk X hjk )/N k and S ih jk = X hjk(n ik X ijk)/n k. Liu (2003) proposed the MH type estimator of β i as where K c 1 L ih = log L i = 1 r Rjk ih k=1 j=1 r h=1,h i L ih K c 1 log Sjk ih k=1 j=1 r 1 L rh h=1, i = 1,..., r 1, (2), i = 1,..., r, h = 1,..., r, and i h. 4
5 Furthermore, the dually consistent variance and covariance estimators for { L i } follow immediately from Greenland (1989) and Liu (2003) as ˆ Cov( L i, L h ) = 1 r 2 (U + ih U + ir U + rh + U + rr ) ˆ Var( L i ) = 1 r 2 (U i++ 2U + ir + U r++), (3) where U ihg = 0 if i = h or g, U ih + = U i++ if i = h, and U ih + = U hi + = U +ih U ih+ U hi+ + U ihh if i h. The form of U ihg is given in Appendix A. Liu (2003) gave the proof of dual consistency for the variance and covariance estimators. For sparse data such as that in Table 1, the MH-type estimators (2) of the cumulative log odds ratios {β i } perform better than the ML estimators. 3. PROPORTIONAL ODDS MODEL WITH RANDOM EFFECTS When comparing treatments on an ordinal response with stratified data, it is common to use Model (1) that assumes a lack of interaction between the treatment effects and strata. That is, the cumulative odds ratios used to describe the conditional association between treatment and response are the same for each clinic center. To check the homogeneity of cumulative odds ratios, we might use ordinary Pearson or likelihood-ratio goodness-offit tests for the corresponding model. Such tests are appropriate only under the ordinary asymptotics, which is not the case for Table 1. In a clinical trial conducted to compare treatments among several clinical centers, the true treatment effects might vary due to some other factors, such as age among subjects at different centers. Therefore, it is plausible to estimate the degree of the heterogeneity for the cumulative odds ratios across the centers. Hartzel, Liu and Agresti (2001) used random effects terms to describe the variability for the cumulative log odds ratios for two treatments. When the strata are a sample, such as a sample of clinical centers, it is natural to treat the strata effects or the cumulative log odds ratios as random effects across strata. Consider a proportional odds model allowing random effects terms as ( ) π ijk log = α 1 πijk j + c k + b ik i = 1,..., r, j = 1,..., c 1, k = 1,..., K, (4) 5
6 where b rk = 0 for all k and {b 1k,..., b r 1,k } are a vector of correlated random effects. In the model, {c 1,..., c K } are independent observations from a N(γ, σ c ) and {b i1,..., b ik } are independent observations from a N(β i, σ βi ) for all i = 1,..., r 1. The model is a special case of a multivariate generalized linear mixed model for ordinal responses (Tutz and Hennevogl, 1996). To obtain the likelihood function we construct the usual product of multinomials and then integrate out the random effects with respect to the random effects distribution. Let u k = (c k, b 1k,..., b r 1,k ) be the random effects having a multivariate normal distribution with mean β = (γ, β 1,..., β r 1 ) and the covariance matrix Σ. That is, u k N[β, Σ]. Let g(u; β, Σ) denote the multivariate normal density function with mean β and covariance matrix Σ. The likelihood function has the form K r c L(α, β, Σ) = (π ijk ) X ijk g(u k ; β, Σ)du k, k=1 i=1 j=1 where π i1k = exp[ (α 1 + c k + b ik )] π ijk = exp[ (α j + c k + b ik )] exp[ (α j 1 + c k + b ik )], j = 2,..., c 1 π ick = exp[ (α c 1 + c k + b ik )] To fit Model (4), one can consider Hedeker and Gibbons (1994) procedures. They maximized the likelihood after approximating the integrals by Gauss-Hermite quadrature. Alternatively, the procedure PROC NLMIXED in SAS for fitting generalized linear mixed models using adaptive Gauss-Hermite quadrature is also available to fit the proportional odds models with random effects. See Hartzel, Liu, and Agresti (2001) for the details of SAS codes. However, fitting the proportional odds model with random effects may not be suitable for highly sparse data. The fitting algorithm often fails to converge when the data are sparse and when there are many random effects. In our experience, the fitting algorithm for SAS PROC NLMIXED is often successful when the number of random effects is less than 3. 6
7 For Model (4), we describe the conditional association between treatments and response using the estimate of means {β 1, β 2,..., β r 1 }. The degree of the heterogeneity for the cumulative log odds ratios across the centers is given by the estimate of standard deviations {σ β1, σ β2,..., σ βr 1 }. 4. A DELETION INFLUENCE MEASURE There are several ways to check for the homogeneity of cumulative odds ratios or to describe the heterogeneity among them. For instance, we might use an overall goodness-offit test to check the homogeneity. In the proportional odds model with random effects (4), we can use the variance estimators to indicate the level of heterogeneity among the cumulative log odds ratios across centers. When the homogeneity doesn t hold, the next question that arises is to identify which pieces of data contribute to the heterogeneity most. Sometimes, even though the heterogeneity is minor in magnitude and perhaps not even significant in the sample according to a statistical test, it is always interesting to get the detail of the heterogeneity for the cumulative log odds ratios among multi-centers. Suppose we replace {β i } by {β ik } in the proportional odds model (1). This model allows the cumulative odds ratio to vary among centers. The variability in {β i1, β i2,..., β ik } indicates the heterogeneity of the conditional associations between treatments and response across centers for all i = 1,..., r 1. However, when there are few observations in a particular center (say center k), the ML estimate of {β 1k, β 2k,..., β r 1,k } is not reliable. The estimate can approach to either negative infinity or positive infinity because of the many zero counts within the center. A similar problem occurs if we use the Mantel-Haenszel type estimator (2) to estimate the cumulative odds ratios for each center. For the example in Table 1, each center has very few patients. It is not appropriate to estimate the center-specific cumulative odds ratios in the sense that the treatment effects obtained separately from each center provide little information. Consequently, the detail of heterogeneity is not available based on the estimates of the center-specific cumulative odds ratios. Instead, this article proposes an influence measure that determines the strength of the heterogeneity of the cumulative log odds ratios across different centers based on the MH type 7
8 estimators (2). The proposed influence measure has a similar form as the Cook s distance (Cook and Weisberg, 1982) for the ordinary linear regression models. For the linear regression models, the Cook s distance is a common diagnostics method used to detect a goodness-of-fit of the model and also to identify possibly aberrant data points in multivariate analysis. In general, the Cook s distance D k has the form D k = (ˆθ ˆθ (k) ) T ˆ Cov(ˆθ) 1 (ˆθ ˆθ (k) ) where ˆθ is the vector of estimates based on all data points and ˆθ (k) is the vector of estimates when the kth data point is deleted. For the stratified clinical trials, we are interested which clinical center has a strong influence to the treatment effects. A clinical center is said to be influential if its deletion has a relatively large influence on the inference measure. For the multi-center clinical trials, if one center (e.g., kth) is deleted, the effect of deleting the kth center on the estimates of { L i } is to yield the estimates of { L i(k) }. The proposed influence measure is defined as C k = ( L L (k) ) T Cov( ˆ L) 1 ( L L (k) ), k = 1,..., K, (5) where the vector L = ( L 1, L 2,..., L r 1 ) is the MH estimates of the cumulative log odds ratios and Cov( ˆ L) is the estimate of the covariance matrix of ( L 1, L 2,..., L r 1 ). The form of { L i } is given on (2) and their variance and covariance estimates are given on (3). The effect C k of deletion is a quadratic form measuring the overall difference between the vector L and the L (k). A relatively large value of C k implies that the kth clinical center has a high influence on the cumulative log odds ratios. 5. EXAMPLE ratios. For the example in Table 1, we use an MH type method to estimate the cumulative odds We also describe the heterogeneity for the cumulative odds ratios among centers based on the two strategies discussed in Sections 3 and Cumulative Odds Ratios Estimates in a Proportional Odds Model 8
9 Using the MH type estimates (2) to describe the conditional relationship between the treatments and the patients change in condition given each center, we have L 13 = and L 23 = with standard error estimates of and respectively. That is, the odds of improving using the active drug with dose 2mg (or 10mg) are estimated to be exp(0.640) = 1.90 (or exp(1.063) = 2.90) times the odds for the placebo, given each center. In comparison, the ordinary ML estimates of ˆβ 1 and ˆβ 2 for model (1) equal with an estimated standard error of 0.343, and with an estimated standard error of 0.355, respectively. We tend to obtain a larger parameter estimate from the ML fitting when the number of observations in each center is small First Diagnostics Strategy Using a Random Effects Model Using the proportional odds model with random effects (4), the fitting algorithm fails to converge because of the sparseness of the data and more than 2 random effects in the model. To illustrate the strategy discussed in Section 3 in this example, we combine the response scales 3 and 4 to make the data less sparse. Since r = 3 in this example, there are 3 random effects in model (4), including (c k, b 1k, b 2k ). To reduce the number of random effects, we let {b 2k } degenerate to its mean β 2. We treat {c 1, c 2,..., c K } as independent observations from a N(γ, σ c ) and {b 11, b 12,..., b 1K } as independent observations from a N(β 1, σ β1 ). The random effects (c k, b 1k ) are correlated with covariance σ cβ. Using SAS PROC NLMIXED, the estimate of β 1 is with a standard error of and the estimate for the variability of {b 11, b 12,..., b 1K } is ˆσ β1 = with a standard error of Using the proportional odds model with random effects, the odds of improving using the active drug with dose 2mg are estimated to be exp(0.517) = 1.68 times the odds for the placebo across centers. Although the standard deviation σ β1 is not significantly different form zero, the strength of heterogeneity is large compared to the level of ˆβ 1. The estimate of β 2 is 1.185, with a standard error of 0.325, that is, the odds of improving using the active drug with dose 10mg are estimated to be exp(1.185) = 3.27 times the odds for the placebo. Comparing to the MH type estimates of the cumulative odds ratio, the model (4) produces similar estimates of the conditional associations between treatments and response. Furthermore, the random effects 9
10 model gives the overall estimate for the level of heterogeneity in the treatment effects, that is ˆσ β Second Diagnostics Strategy Using the Influence Measure On the other hand, we can consider the influence measure (5) for the cumulative log odds ratios in each center. Table 2 lists the influence measure C k and the estimates of the cumulative log odds ratios when each of the centers is deleted. The notations { L i(k) } denote the cumulative log odds ratios when the kth center is deleted for i = 1, 2 and k = 1,..., 21. It is clear from Figure 1 that clinical centers 13, 15, 3, and 17 have a larger influence measure compared to the others. This might suggest that the cumulative log odds ratios for those centers are different from the others. It could be due to error, or some other factors (e.g., age), which is worth investigation. ACKNOWLEDGMENT We would like to thank Megan Clark for helpful comments. 10
11 BIBLIOGRAPHY Andersen, E. B. (1980). Discrete statistical methods with social science applications. New York: North-Holland. Andersen, E. B. (1992). Diagnostics in categorical data analysis. 54(3): J. R. Statist. Soc. B Atkinson, A. C. (1985). Plot, transformations and regression. Oxford: Clarendon. Cook, R. D. and Weisberg, S. (1982). Residuals and influence in regression. New York: Chapman and Hall. Ghosh, M. (1995). Inconsistent MLE for the Rasch model. Statist. Probab. Lett. 23: Greenland, S. (1989). Generalized Mantel-Haenszel estimators for K 2 J tables. Biometrics 45: Hartzel, J., Liu, I-M. and Agresti, A. (2001). Describing heterogeneous effects in stratified ordinal contingency tables, with application to multi-center clinical trials. Comput. Statist. Data Anal. 35: Hedeker, D. and Gibbons, R. D. (1994). multilevel analysis. Biometrics 50: A random-effects ordinal regression model for Liu, I-M. and Agresti, A. (1996). Mantel-Haenszel-type inference for cumulative odds ratios with a stratified ordinal responses. Biometrics 52: Liu, I. (2003). Describing ordinal odds ratios for stratified r c tables. Biometrical J. 45: Mantel, N. and Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies of disease. J. Natl. Cancer Inst. 22: McCullagh, P. (1980). Regression models for ordinal data. J. R. Statist. Soc. B 42:
12 Neyman, J. and Scott, E. L. (1948). observations. Econometrika 16:1-22. Consistent estimates based on partially consistent Pregibon, D. (1981). Logistic regression diagnostics. Ann. Statist. 9: Tutz, G. and Hennevogl. W. (1996). Random effects in ordinal l regression models. Comput. Statist. Data Anal. 22: Wang, D. Q. and Critchley, F. (2000). Multiple deletion measures and conditional influence in regression models. Commun. Statist. Theory Meth. 29: Wang, D. Q., Critchley, F. and Liu, I. (2004). Diagnostics analysis and perturbations in a clustered sampling model. Commun. Statist. Theory Meth. 33: Wang, D. Q., Critchley, F. and Smith, P. J. (2003). The multiple sets of deletion measurer and masking in regression. Commun. Statist. Theory Meth. 32:
13 APPENDIX A The form of U ihg is as follows: where ˆθ ia = ( k U ihg = j 1 ˆθ 2 ih K 1 ˆθ ih ˆθig R ia jk )/( k c 1 k=1 j=1 K k=1 j=1 j ˆφ ih jk (ˆθ ih )+2 k [ K c 1 c 1 k=1 j=1 S ih jk c 1 ˆφ ih jsk (ˆθ ih ) j<s ] 2, if i h = g K ˆφ ihg c 1 jk (ˆθ ih )+ ˆφ ihig jsk (ˆθ ih,ˆθ ig ) k=1 j s ( K c 1 k=1 j=1 S ih jk )( K c 1 k=1 j=1 S ig jk Sjk ia ) for all a i and ), if i h g, ˆφ ih jk(θ) = 1 [(n Nk 2 ik Xijk)X hjkθ (n ik Xijk)(n hk Xhjk) (Xijk + X hjk )θ + (n hk Xhjk )X ijk ] ˆφ ih jsk (θ) = 1 [(n Nk 2 ik Xisk )X 2 hjk θ2 + (n ik Xisk )(n hk Xhsk ) (Xijk + Xhjk)θ + (n hk Xhsk)X isk] ˆφ ihg jk (θ) = 1 [(n Nk 2 gk XijkX hjk n ik XhjkX gjk)θ +(n hk n gk Xijk n gkxijk X hjk )] ˆφ ihig jsk (θ ih, θ ig ) = n ik θ ih N 2 k n ik θ ig N 2 k [ X hjk (n gk X gsk )] if j < s [ X gsk (n hk X hjk )] If j > s. 13
14 Figure 1: Influence Measure for Table 1 influence measure clinical center 14
15 Table 1: Doctors Evaluations of patients suffering from asthma Response Response Center Drug Center Drug mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg mg mg Placebo Placebo mg mg Placebo Note: Response is scaled from better (1) to worse (4). 15
16 Table 2: Influence Measure center k C k L1(k) L2(k)
Describing Stratified Multiple Responses for Sparse Data
Describing Stratified Multiple Responses for Sparse Data Ivy Liu School of Mathematical and Computing Sciences Victoria University Wellington, New Zealand June 28, 2004 SUMMARY Surveys often contain qualitative
More informationFitting stratified proportional odds models by amalgamating conditional likelihoods
STATISTICS IN MEDICINE Statist. Med. 2008; 27:4950 4971 Published online 10 July 2008 in Wiley InterScience (www.interscience.wiley.com).3325 Fitting stratified proportional odds models by amalgamating
More informationLinks Between Binary and Multi-Category Logit Item Response Models and Quasi-Symmetric Loglinear Models
Links Between Binary and Multi-Category Logit Item Response Models and Quasi-Symmetric Loglinear Models Alan Agresti Department of Statistics University of Florida Gainesville, Florida 32611-8545 July
More information2 Describing Contingency Tables
2 Describing Contingency Tables I. Probability structure of a 2-way contingency table I.1 Contingency Tables X, Y : cat. var. Y usually random (except in a case-control study), response; X can be random
More informationReview. Timothy Hanson. Department of Statistics, University of South Carolina. Stat 770: Categorical Data Analysis
Review Timothy Hanson Department of Statistics, University of South Carolina Stat 770: Categorical Data Analysis 1 / 22 Chapter 1: background Nominal, ordinal, interval data. Distributions: Poisson, binomial,
More informationGood Confidence Intervals for Categorical Data Analyses. Alan Agresti
Good Confidence Intervals for Categorical Data Analyses Alan Agresti Department of Statistics, University of Florida visiting Statistics Department, Harvard University LSHTM, July 22, 2011 p. 1/36 Outline
More informationRepeated ordinal measurements: a generalised estimating equation approach
Repeated ordinal measurements: a generalised estimating equation approach David Clayton MRC Biostatistics Unit 5, Shaftesbury Road Cambridge CB2 2BW April 7, 1992 Abstract Cumulative logit and related
More informationAnalysis of matched case control data with multiple ordered disease states: Possible choices and comparisons
STATISTICS IN MEDICINE Statist. Med. 2007; 26:3240 3257 Published online 5 January 2007 in Wiley InterScience (www.interscience.wiley.com).2790 Analysis of matched case control data with multiple ordered
More informationDescribing Contingency tables
Today s topics: Describing Contingency tables 1. Probability structure for contingency tables (distributions, sensitivity/specificity, sampling schemes). 2. Comparing two proportions (relative risk, odds
More informationSections 2.3, 2.4. Timothy Hanson. Department of Statistics, University of South Carolina. Stat 770: Categorical Data Analysis 1 / 21
Sections 2.3, 2.4 Timothy Hanson Department of Statistics, University of South Carolina Stat 770: Categorical Data Analysis 1 / 21 2.3 Partial association in stratified 2 2 tables In describing a relationship
More informationSTAT 5500/6500 Conditional Logistic Regression for Matched Pairs
STAT 5500/6500 Conditional Logistic Regression for Matched Pairs The data for the tutorial came from support.sas.com, The LOGISTIC Procedure: Conditional Logistic Regression for Matched Pairs Data :: SAS/STAT(R)
More informationThree-Way Contingency Tables
Newsom PSY 50/60 Categorical Data Analysis, Fall 06 Three-Way Contingency Tables Three-way contingency tables involve three binary or categorical variables. I will stick mostly to the binary case to keep
More informationNATIONAL UNIVERSITY OF SINGAPORE EXAMINATION (SOLUTIONS) ST3241 Categorical Data Analysis. (Semester II: )
NATIONAL UNIVERSITY OF SINGAPORE EXAMINATION (SOLUTIONS) Categorical Data Analysis (Semester II: 2010 2011) April/May, 2011 Time Allowed : 2 Hours Matriculation No: Seat No: Grade Table Question 1 2 3
More informationLecture 10: Introduction to Logistic Regression
Lecture 10: Introduction to Logistic Regression Ani Manichaikul amanicha@jhsph.edu 2 May 2007 Logistic Regression Regression for a response variable that follows a binomial distribution Recall the binomial
More informationGeneralized Linear Models
York SPIDA John Fox Notes Generalized Linear Models Copyright 2010 by John Fox Generalized Linear Models 1 1. Topics I The structure of generalized linear models I Poisson and other generalized linear
More informationA Regression Model For Recurrent Events With Distribution Free Correlation Structure
A Regression Model For Recurrent Events With Distribution Free Correlation Structure J. Pénichoux(1), A. Latouche(2), T. Moreau(1) (1) INSERM U780 (2) Université de Versailles, EA2506 ISCB - 2009 - Prague
More informationSTAT 5500/6500 Conditional Logistic Regression for Matched Pairs
STAT 5500/6500 Conditional Logistic Regression for Matched Pairs Motivating Example: The data we will be using comes from a subset of data taken from the Los Angeles Study of the Endometrial Cancer Data
More informationChapter 2: Describing Contingency Tables - II
: Describing Contingency Tables - II Dipankar Bandyopadhyay Department of Biostatistics, Virginia Commonwealth University BIOS 625: Categorical Data & GLM [Acknowledgements to Tim Hanson and Haitao Chu]
More informationH-LIKELIHOOD ESTIMATION METHOOD FOR VARYING CLUSTERED BINARY MIXED EFFECTS MODEL
H-LIKELIHOOD ESTIMATION METHOOD FOR VARYING CLUSTERED BINARY MIXED EFFECTS MODEL Intesar N. El-Saeiti Department of Statistics, Faculty of Science, University of Bengahzi-Libya. entesar.el-saeiti@uob.edu.ly
More informationMulti-Level Test of Independence for 2 X 2 Contingency Table using Cochran and Mantel Haenszel Statistics
IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. Issue 8, August 015. ISSN 348 7968 Multi-Level Test of Independence for X Contingency Table using Cochran and Mantel
More informationGeneralized Linear Model under the Extended Negative Multinomial Model and Cancer Incidence
Generalized Linear Model under the Extended Negative Multinomial Model and Cancer Incidence Sunil Kumar Dhar Center for Applied Mathematics and Statistics, Department of Mathematical Sciences, New Jersey
More informationPseudo-score confidence intervals for parameters in discrete statistical models
Biometrika Advance Access published January 14, 2010 Biometrika (2009), pp. 1 8 C 2009 Biometrika Trust Printed in Great Britain doi: 10.1093/biomet/asp074 Pseudo-score confidence intervals for parameters
More informationGeneralized Linear Models: An Introduction
Applied Statistics With R Generalized Linear Models: An Introduction John Fox WU Wien May/June 2006 2006 by John Fox Generalized Linear Models: An Introduction 1 A synthesis due to Nelder and Wedderburn,
More informationLecture 23. November 15, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationSTAT 705 Generalized linear mixed models
STAT 705 Generalized linear mixed models Timothy Hanson Department of Statistics, University of South Carolina Stat 705: Data Analysis II 1 / 24 Generalized Linear Mixed Models We have considered random
More informationMore Statistics tutorial at Logistic Regression and the new:
Logistic Regression and the new: Residual Logistic Regression 1 Outline 1. Logistic Regression 2. Confounding Variables 3. Controlling for Confounding Variables 4. Residual Linear Regression 5. Residual
More informationContrasting Marginal and Mixed Effects Models Recall: two approaches to handling dependence in Generalized Linear Models:
Contrasting Marginal and Mixed Effects Models Recall: two approaches to handling dependence in Generalized Linear Models: Marginal models: based on the consequences of dependence on estimating model parameters.
More informationST3241 Categorical Data Analysis I Generalized Linear Models. Introduction and Some Examples
ST3241 Categorical Data Analysis I Generalized Linear Models Introduction and Some Examples 1 Introduction We have discussed methods for analyzing associations in two-way and three-way tables. Now we will
More informationBinomial Model. Lecture 10: Introduction to Logistic Regression. Logistic Regression. Binomial Distribution. n independent trials
Lecture : Introduction to Logistic Regression Ani Manichaikul amanicha@jhsph.edu 2 May 27 Binomial Model n independent trials (e.g., coin tosses) p = probability of success on each trial (e.g., p =! =
More informationGeneralized Linear Models (GLZ)
Generalized Linear Models (GLZ) Generalized Linear Models (GLZ) are an extension of the linear modeling process that allows models to be fit to data that follow probability distributions other than the
More informationSTAC51: Categorical data Analysis
STAC51: Categorical data Analysis Mahinda Samarakoon January 26, 2016 Mahinda Samarakoon STAC51: Categorical data Analysis 1 / 32 Table of contents Contingency Tables 1 Contingency Tables Mahinda Samarakoon
More informationTests of independence for censored bivariate failure time data
Tests of independence for censored bivariate failure time data Abstract Bivariate failure time data is widely used in survival analysis, for example, in twins study. This article presents a class of χ
More informationOutline of GLMs. Definitions
Outline of GLMs Definitions This is a short outline of GLM details, adapted from the book Nonparametric Regression and Generalized Linear Models, by Green and Silverman. The responses Y i have density
More informationCategorical data analysis Chapter 5
Categorical data analysis Chapter 5 Interpreting parameters in logistic regression The sign of β determines whether π(x) is increasing or decreasing as x increases. The rate of climb or descent increases
More informationAccounting for Baseline Observations in Randomized Clinical Trials
Accounting for Baseline Observations in Randomized Clinical Trials Scott S Emerson, MD, PhD Department of Biostatistics, University of Washington, Seattle, WA 9895, USA August 5, 0 Abstract In clinical
More informationModels for Longitudinal Analysis of Binary Response Data for Identifying the Effects of Different Treatments on Insomnia
Applied Mathematical Sciences, Vol. 4, 2010, no. 62, 3067-3082 Models for Longitudinal Analysis of Binary Response Data for Identifying the Effects of Different Treatments on Insomnia Z. Rezaei Ghahroodi
More informationMultinomial Logistic Regression Models
Stat 544, Lecture 19 1 Multinomial Logistic Regression Models Polytomous responses. Logistic regression can be extended to handle responses that are polytomous, i.e. taking r>2 categories. (Note: The word
More informationMS&E 226: Small Data. Lecture 11: Maximum likelihood (v2) Ramesh Johari
MS&E 226: Small Data Lecture 11: Maximum likelihood (v2) Ramesh Johari ramesh.johari@stanford.edu 1 / 18 The likelihood function 2 / 18 Estimating the parameter This lecture develops the methodology behind
More informationContinuous Time Survival in Latent Variable Models
Continuous Time Survival in Latent Variable Models Tihomir Asparouhov 1, Katherine Masyn 2, Bengt Muthen 3 Muthen & Muthen 1 University of California, Davis 2 University of California, Los Angeles 3 Abstract
More informationTesting for independence in J K contingency tables with complex sample. survey data
Biometrics 00, 1 23 DOI: 000 November 2014 Testing for independence in J K contingency tables with complex sample survey data Stuart R. Lipsitz 1,, Garrett M. Fitzmaurice 2, Debajyoti Sinha 3, Nathanael
More informationMeasure for No Three-Factor Interaction Model in Three-Way Contingency Tables
American Journal of Biostatistics (): 7-, 00 ISSN 948-9889 00 Science Publications Measure for No Three-Factor Interaction Model in Three-Way Contingency Tables Kouji Yamamoto, Kyoji Hori and Sadao Tomizawa
More informationGoodness-of-Fit Tests for the Ordinal Response Models with Misspecified Links
Communications of the Korean Statistical Society 2009, Vol 16, No 4, 697 705 Goodness-of-Fit Tests for the Ordinal Response Models with Misspecified Links Kwang Mo Jeong a, Hyun Yung Lee 1, a a Department
More informationStatistical Methods in Clinical Trials Categorical Data
Statistical Methods in Clinical Trials Categorical Data Types of Data quantitative Continuous Blood pressure Time to event Categorical sex qualitative Discrete No of relapses Ordered Categorical Pain level
More informationLecture 15 (Part 2): Logistic Regression & Common Odds Ratio, (With Simulations)
Lecture 15 (Part 2): Logistic Regression & Common Odds Ratio, (With Simulations) Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology
More informationSTA 4504/5503 Sample Exam 1 Spring 2011 Categorical Data Analysis. 1. Indicate whether each of the following is true (T) or false (F).
STA 4504/5503 Sample Exam 1 Spring 2011 Categorical Data Analysis 1. Indicate whether each of the following is true (T) or false (F). (a) T In 2 2 tables, statistical independence is equivalent to a population
More information11. Generalized Linear Models: An Introduction
Sociology 740 John Fox Lecture Notes 11. Generalized Linear Models: An Introduction Copyright 2014 by John Fox Generalized Linear Models: An Introduction 1 1. Introduction I A synthesis due to Nelder and
More informationAn Overview of Methods in the Analysis of Dependent Ordered Categorical Data: Assumptions and Implications
WORKING PAPER SERIES WORKING PAPER NO 7, 2008 Swedish Business School at Örebro An Overview of Methods in the Analysis of Dependent Ordered Categorical Data: Assumptions and Implications By Hans Högberg
More informationMultivariate Extensions of McNemar s Test
Multivariate Extensions of McNemar s Test Bernhard Klingenberg Department of Mathematics and Statistics, Williams College Williamstown, MA 01267, U.S.A. e-mail: bklingen@williams.edu and Alan Agresti Department
More informationPubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH
PubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH The First Step: SAMPLE SIZE DETERMINATION THE ULTIMATE GOAL The most important, ultimate step of any of clinical research is to do draw inferences;
More informationFractional Imputation in Survey Sampling: A Comparative Review
Fractional Imputation in Survey Sampling: A Comparative Review Shu Yang Jae-Kwang Kim Iowa State University Joint Statistical Meetings, August 2015 Outline Introduction Fractional imputation Features Numerical
More informationLog-linear multidimensional Rasch model for capture-recapture
Log-linear multidimensional Rasch model for capture-recapture Elvira Pelle, University of Milano-Bicocca, e.pelle@campus.unimib.it David J. Hessen, Utrecht University, D.J.Hessen@uu.nl Peter G.M. Van der
More informationAPPENDIX B Sample-Size Calculation Methods: Classical Design
APPENDIX B Sample-Size Calculation Methods: Classical Design One/Paired - Sample Hypothesis Test for the Mean Sign test for median difference for a paired sample Wilcoxon signed - rank test for one or
More informationHIERARCHICAL BAYESIAN ANALYSIS OF BINARY MATCHED PAIRS DATA
Statistica Sinica 1(2), 647-657 HIERARCHICAL BAYESIAN ANALYSIS OF BINARY MATCHED PAIRS DATA Malay Ghosh, Ming-Hui Chen, Atalanta Ghosh and Alan Agresti University of Florida, Worcester Polytechnic Institute
More informationMulti-level Models: Idea
Review of 140.656 Review Introduction to multi-level models The two-stage normal-normal model Two-stage linear models with random effects Three-stage linear models Two-stage logistic regression with random
More informationOn Properties of QIC in Generalized. Estimating Equations. Shinpei Imori
On Properties of QIC in Generalized Estimating Equations Shinpei Imori Graduate School of Engineering Science, Osaka University 1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan E-mail: imori.stat@gmail.com
More information,..., θ(2),..., θ(n)
Likelihoods for Multivariate Binary Data Log-Linear Model We have 2 n 1 distinct probabilities, but we wish to consider formulations that allow more parsimonious descriptions as a function of covariates.
More informationSTA 4504/5503 Sample Exam 1 Spring 2011 Categorical Data Analysis. 1. Indicate whether each of the following is true (T) or false (F).
STA 4504/5503 Sample Exam 1 Spring 2011 Categorical Data Analysis 1. Indicate whether each of the following is true (T) or false (F). (a) (b) (c) (d) (e) In 2 2 tables, statistical independence is equivalent
More informationFULL LIKELIHOOD INFERENCES IN THE COX MODEL
October 20, 2007 FULL LIKELIHOOD INFERENCES IN THE COX MODEL BY JIAN-JIAN REN 1 AND MAI ZHOU 2 University of Central Florida and University of Kentucky Abstract We use the empirical likelihood approach
More informationPQL Estimation Biases in Generalized Linear Mixed Models
PQL Estimation Biases in Generalized Linear Mixed Models Woncheol Jang Johan Lim March 18, 2006 Abstract The penalized quasi-likelihood (PQL) approach is the most common estimation procedure for the generalized
More informationQUANTIFYING PQL BIAS IN ESTIMATING CLUSTER-LEVEL COVARIATE EFFECTS IN GENERALIZED LINEAR MIXED MODELS FOR GROUP-RANDOMIZED TRIALS
Statistica Sinica 15(05), 1015-1032 QUANTIFYING PQL BIAS IN ESTIMATING CLUSTER-LEVEL COVARIATE EFFECTS IN GENERALIZED LINEAR MIXED MODELS FOR GROUP-RANDOMIZED TRIALS Scarlett L. Bellamy 1, Yi Li 2, Xihong
More informationChapter 1 Statistical Inference
Chapter 1 Statistical Inference causal inference To infer causality, you need a randomized experiment (or a huge observational study and lots of outside information). inference to populations Generalizations
More informationTesting for group differences in brain functional connectivity
Testing for group differences in brain functional connectivity Junghi Kim, Wei Pan, for ADNI Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455 Banff Feb
More informationSolutions for Examination Categorical Data Analysis, March 21, 2013
STOCKHOLMS UNIVERSITET MATEMATISKA INSTITUTIONEN Avd. Matematisk statistik, Frank Miller MT 5006 LÖSNINGAR 21 mars 2013 Solutions for Examination Categorical Data Analysis, March 21, 2013 Problem 1 a.
More informationModeling and Measuring Association for Ordinal Data
Modeling and Measuring Association for Ordinal Data A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in
More informationGeneralized Linear Models. Kurt Hornik
Generalized Linear Models Kurt Hornik Motivation Assuming normality, the linear model y = Xβ + e has y = β + ε, ε N(0, σ 2 ) such that y N(μ, σ 2 ), E(y ) = μ = β. Various generalizations, including general
More informationCOMPOSITIONAL IDEAS IN THE BAYESIAN ANALYSIS OF CATEGORICAL DATA WITH APPLICATION TO DOSE FINDING CLINICAL TRIALS
COMPOSITIONAL IDEAS IN THE BAYESIAN ANALYSIS OF CATEGORICAL DATA WITH APPLICATION TO DOSE FINDING CLINICAL TRIALS M. Gasparini and J. Eisele 2 Politecnico di Torino, Torino, Italy; mauro.gasparini@polito.it
More informationMantel-Haenszel Test Statistics. for Correlated Binary Data. Department of Statistics, North Carolina State University. Raleigh, NC
Mantel-Haenszel Test Statistics for Correlated Binary Data by Jie Zhang and Dennis D. Boos Department of Statistics, North Carolina State University Raleigh, NC 27695-8203 tel: (919) 515-1918 fax: (919)
More informationAccounting for Baseline Observations in Randomized Clinical Trials
Accounting for Baseline Observations in Randomized Clinical Trials Scott S Emerson, MD, PhD Department of Biostatistics, University of Washington, Seattle, WA 9895, USA October 6, 0 Abstract In clinical
More informationLongitudinal analysis of ordinal data
Longitudinal analysis of ordinal data A report on the external research project with ULg Anne-Françoise Donneau, Murielle Mauer June 30 th 2009 Generalized Estimating Equations (Liang and Zeger, 1986)
More informationStat 642, Lecture notes for 04/12/05 96
Stat 642, Lecture notes for 04/12/05 96 Hosmer-Lemeshow Statistic The Hosmer-Lemeshow Statistic is another measure of lack of fit. Hosmer and Lemeshow recommend partitioning the observations into 10 equal
More informationInstructions: Closed book, notes, and no electronic devices. Points (out of 200) in parentheses
ISQS 5349 Final Spring 2011 Instructions: Closed book, notes, and no electronic devices. Points (out of 200) in parentheses 1. (10) What is the definition of a regression model that we have used throughout
More informationAsymptotic equivalence of paired Hotelling test and conditional logistic regression
Asymptotic equivalence of paired Hotelling test and conditional logistic regression Félix Balazard 1,2 arxiv:1610.06774v1 [math.st] 21 Oct 2016 Abstract 1 Sorbonne Universités, UPMC Univ Paris 06, CNRS
More informationDiscrete Multivariate Statistics
Discrete Multivariate Statistics Univariate Discrete Random variables Let X be a discrete random variable which, in this module, will be assumed to take a finite number of t different values which are
More informationLogistic regression: Miscellaneous topics
Logistic regression: Miscellaneous topics April 11 Introduction We have covered two approaches to inference for GLMs: the Wald approach and the likelihood ratio approach I claimed that the likelihood ratio
More informationPROD. TYPE: COM. Simple improved condence intervals for comparing matched proportions. Alan Agresti ; and Yongyi Min UNCORRECTED PROOF
pp: --2 (col.fig.: Nil) STATISTICS IN MEDICINE Statist. Med. 2004; 2:000 000 (DOI: 0.002/sim.8) PROD. TYPE: COM ED: Chandra PAGN: Vidya -- SCAN: Nil Simple improved condence intervals for comparing matched
More informationAssociation studies and regression
Association studies and regression CM226: Machine Learning for Bioinformatics. Fall 2016 Sriram Sankararaman Acknowledgments: Fei Sha, Ameet Talwalkar Association studies and regression 1 / 104 Administration
More informationGenerating Half-normal Plot for Zero-inflated Binomial Regression
Paper SP05 Generating Half-normal Plot for Zero-inflated Binomial Regression Zhao Yang, Xuezheng Sun Department of Epidemiology & Biostatistics University of South Carolina, Columbia, SC 29208 SUMMARY
More informationA class of latent marginal models for capture-recapture data with continuous covariates
A class of latent marginal models for capture-recapture data with continuous covariates F Bartolucci A Forcina Università di Urbino Università di Perugia FrancescoBartolucci@uniurbit forcina@statunipgit
More informationGeneralized Linear Models I
Statistics 203: Introduction to Regression and Analysis of Variance Generalized Linear Models I Jonathan Taylor - p. 1/16 Today s class Poisson regression. Residuals for diagnostics. Exponential families.
More informationGeneralized logit models for nominal multinomial responses. Local odds ratios
Generalized logit models for nominal multinomial responses Categorical Data Analysis, Summer 2015 1/17 Local odds ratios Y 1 2 3 4 1 π 11 π 12 π 13 π 14 π 1+ X 2 π 21 π 22 π 23 π 24 π 2+ 3 π 31 π 32 π
More informationBios 6649: Clinical Trials - Statistical Design and Monitoring
Bios 6649: Clinical Trials - Statistical Design and Monitoring Spring Semester 2015 John M. Kittelson Department of Biostatistics & Informatics Colorado School of Public Health University of Colorado Denver
More informationLecture 25. Ingo Ruczinski. November 24, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University
Lecture 25 Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University November 24, 2015 1 2 3 4 5 6 7 8 9 10 11 1 Hypothesis s of homgeneity 2 Estimating risk
More information2.6.3 Generalized likelihood ratio tests
26 HYPOTHESIS TESTING 113 263 Generalized likelihood ratio tests When a UMP test does not exist, we usually use a generalized likelihood ratio test to verify H 0 : θ Θ against H 1 : θ Θ\Θ It can be used
More informationTutz: Modelling of repeated ordered measurements by isotonic sequential regression
Tutz: Modelling of repeated ordered measurements by isotonic sequential regression Sonderforschungsbereich 386, Paper 298 (2002) Online unter: http://epub.ub.uni-muenchen.de/ Projektpartner Modelling of
More informationCHL 5225 H Crossover Trials. CHL 5225 H Crossover Trials
CHL 55 H Crossover Trials The Two-sequence, Two-Treatment, Two-period Crossover Trial Definition A trial in which patients are randomly allocated to one of two sequences of treatments (either 1 then, or
More informationSTAT 526 Spring Final Exam. Thursday May 5, 2011
STAT 526 Spring 2011 Final Exam Thursday May 5, 2011 Time: 2 hours Name (please print): Show all your work and calculations. Partial credit will be given for work that is partially correct. Points will
More informationA NOTE ON ROBUST ESTIMATION IN LOGISTIC REGRESSION MODEL
Discussiones Mathematicae Probability and Statistics 36 206 43 5 doi:0.75/dmps.80 A NOTE ON ROBUST ESTIMATION IN LOGISTIC REGRESSION MODEL Tadeusz Bednarski Wroclaw University e-mail: t.bednarski@prawo.uni.wroc.pl
More informationPrevious lecture. P-value based combination. Fixed vs random effects models. Meta vs. pooled- analysis. New random effects testing.
Previous lecture P-value based combination. Fixed vs random effects models. Meta vs. pooled- analysis. New random effects testing. Interaction Outline: Definition of interaction Additive versus multiplicative
More informationChapter 1. Modeling Basics
Chapter 1. Modeling Basics What is a model? Model equation and probability distribution Types of model effects Writing models in matrix form Summary 1 What is a statistical model? A model is a mathematical
More information9 Generalized Linear Models
9 Generalized Linear Models The Generalized Linear Model (GLM) is a model which has been built to include a wide range of different models you already know, e.g. ANOVA and multiple linear regression models
More informationStat 579: Generalized Linear Models and Extensions
Stat 579: Generalized Linear Models and Extensions Linear Mixed Models for Longitudinal Data Yan Lu April, 2018, week 15 1 / 38 Data structure t1 t2 tn i 1st subject y 11 y 12 y 1n1 Experimental 2nd subject
More informationGEE for Longitudinal Data - Chapter 8
GEE for Longitudinal Data - Chapter 8 GEE: generalized estimating equations (Liang & Zeger, 1986; Zeger & Liang, 1986) extension of GLM to longitudinal data analysis using quasi-likelihood estimation method
More informationEquivalence of random-effects and conditional likelihoods for matched case-control studies
Equivalence of random-effects and conditional likelihoods for matched case-control studies Ken Rice MRC Biostatistics Unit, Cambridge, UK January 8 th 4 Motivation Study of genetic c-erbb- exposure and
More informationTesting Non-Linear Ordinal Responses in L2 K Tables
RUHUNA JOURNA OF SCIENCE Vol. 2, September 2007, pp. 18 29 http://www.ruh.ac.lk/rjs/ ISSN 1800-279X 2007 Faculty of Science University of Ruhuna. Testing Non-inear Ordinal Responses in 2 Tables eslie Jayasekara
More informationANALYSIS OF ORDINAL SURVEY RESPONSES WITH DON T KNOW
SSC Annual Meeting, June 2015 Proceedings of the Survey Methods Section ANALYSIS OF ORDINAL SURVEY RESPONSES WITH DON T KNOW Xichen She and Changbao Wu 1 ABSTRACT Ordinal responses are frequently involved
More informationEstimating the Marginal Odds Ratio in Observational Studies
Estimating the Marginal Odds Ratio in Observational Studies Travis Loux Christiana Drake Department of Statistics University of California, Davis June 20, 2011 Outline The Counterfactual Model Odds Ratios
More informationSurvival Analysis for Case-Cohort Studies
Survival Analysis for ase-ohort Studies Petr Klášterecký Dept. of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, harles University, Prague, zech Republic e-mail: petr.klasterecky@matfyz.cz
More information36-720: The Rasch Model
36-720: The Rasch Model Brian Junker October 15, 2007 Multivariate Binary Response Data Rasch Model Rasch Marginal Likelihood as a GLMM Rasch Marginal Likelihood as a Log-Linear Model Example For more
More informationA COEFFICIENT OF DETERMINATION FOR LOGISTIC REGRESSION MODELS
A COEFFICIENT OF DETEMINATION FO LOGISTIC EGESSION MODELS ENATO MICELI UNIVESITY OF TOINO After a brief presentation of the main extensions of the classical coefficient of determination ( ), a new index
More informationContingency Tables Part One 1
Contingency Tables Part One 1 STA 312: Fall 2012 1 See last slide for copyright information. 1 / 32 Suggested Reading: Chapter 2 Read Sections 2.1-2.4 You are not responsible for Section 2.5 2 / 32 Overview
More information